Simple customer analytics for small stores

Why small stores need simple analytics

Enterprise analytics platforms assume dedicated data teams, technical expertise, and unlimited time. Small stores have none of these. Founder wears ten hats—analytics can’t consume hours weekly. Simple analytics focus on metrics directly affecting decisions: which marketing works, which products sell, what customers return to buy. Complexity adds nothing when team of one or two lacks time implementing sophisticated insights.

Simple analytics prevent common small store failures. Spending $2,000 monthly on ads without knowing customer acquisition cost—potentially losing money on every customer. Assuming customers return when 85% never make second purchase—building business on false retention assumption. Focusing on traffic growth while ignoring conversion rate—more visitors, same revenue, wasted effort. Three metrics tracked weekly prevent these failures: how much customers cost to acquire, whether they return, what they spend.

The three must-track metrics

1. Customer acquisition cost

What each new customer costs to acquire. Simplest calculation: total marketing spending this month ÷ new customers this month. Example: spent $800 on Facebook ads, acquired 25 new customers = $32 CAC. Track monthly in spreadsheet. Compare to average first purchase value—if CAC exceeds 30-40% of first purchase, acquisition unprofitable short-term (requires repeat purchases for profitability).

Why crucial: prevents unprofitable growth. $80 CAC with $90 average first purchase means barely breaking even—growth dependent on strong repeat purchases. Without knowing CAC, founders often overspend on ads assuming revenue growth justifies spending. Calculate CAC monthly, ensure stays under 40% of average order value for sustainable acquisition.

2. Repeat purchase rate

What percentage of customers buy again. Simple calculation: count customers who made 2+ purchases, divide by customers who purchased 90+ days ago (had time to return). Example: 40 repeat customers ÷ 150 customers from 3+ months ago = 27% repeat rate. Track quarterly—insufficient data monthly for small stores.

Why crucial: reveals whether building sustainable business or one-time buyer treadmill. 10% repeat rate means 90% of customers never return—must constantly acquire new customers replacing departed ones. 40% repeat rate means nearly half return—building loyal base reducing acquisition dependency. Below 20% signals problems: product quality disappointing, forgettable brand, no retention marketing, or wrong customer targeting.

3. Average order value

Typical transaction size. Simplest calculation: monthly revenue ÷ monthly orders. Example: $6,000 revenue, 85 orders = $71 AOV. Track monthly comparing to previous months. Increasing AOV (from $65 to $71 to $78) indicates successful upselling or product mix improvement. Declining AOV suggests discounting too heavily or customers buying cheaper items.

Why crucial: determines revenue targets and profitability. $50 AOV requires 200 monthly orders reaching $10,000 revenue. $75 AOV requires only 133 orders—33% fewer customers needed. Increasing AOV from $60 to $75 (25% increase) same revenue impact as increasing customer count 25%. Often easier growing AOV than growing customer count—add product bundles, raise prices, implement minimum free shipping threshold.

Simple tracking methods

Spreadsheet tracking (15 minutes monthly)

Create simple spreadsheet: Month, New customers, Total customers, Marketing spend, Revenue, Orders. Calculate: CAC = marketing spend ÷ new customers. AOV = revenue ÷ orders. Repeat rate = (total customers − new customers) ÷ customers from 90+ days ago. Monthly updates take 10-15 minutes pulling data from Shopify, WooCommerce, or payment processor.

Advantages: complete control, customizable, free (Google Sheets), permanent record. Small time investment builds critical business understanding. Disadvantages: manual updates required monthly. No daily operational visibility. Requires remembering to update consistently.

Platform basic reports (free, built-in)

Shopify: Analytics → Reports → Customers shows returning customer rate. Reports → Sales shows AOV automatically. Reports → Marketing shows customer acquisition by source. WooCommerce: Analytics → Customers shows customer count and AOV. Reports → Orders shows order trends. Limited compared to dedicated tools but covers essentials for small stores. Zero setup, zero cost.

Advantages: automatic updates, no maintenance, free, simple interface. Disadvantages: limited metric selection, requires logging in to check, no email delivery, basic comparisons only. Sufficient for stores under $10k monthly revenue—upgrade to dedicated analytics when complexity and revenue justify.

Email-delivered daily metrics

Automated reports delivered inbox eliminating dashboard checking. Operational metrics (sales, orders, conversion, traffic) arrive email every morning. Scan in 2-3 minutes during coffee or commute. Team visibility—everyone receives same report maintaining alignment without meetings.

Peasy delivers essential daily operational metrics automatically via email: Sales, Order count, Average order value, Conversion rate, Sessions, Top 5 best-selling products, Top 5 pages, and Top 5 traffic channels—all with automatic comparisons to yesterday, last week, and last year. No dashboard checking required, delivered to your entire team’s inbox. Two-minute morning scan replaces 20-30 minutes daily dashboard checking. Starting at $49/month. Try free for 14 days.

What to do with the data

If CAC too high (over 40% of AOV)

Immediate actions: pause worst-performing ad campaigns (check Facebook Ads Manager or Google Ads for cost per purchase by campaign). Improve conversion rate—add product reviews (increase conversion 15-30%), improve product photos (increase 10-20%), simplify checkout (reduce abandonment 10-15%). Test smaller audiences (narrower targeting often lower CAC despite higher cost per click—better conversion offsets). Improve ad creative (better photos, clearer value proposition, customer testimonials).

Long-term fixes: build email list (email marketing $5-15 CAC versus $40-80 paid ads CAC). Invest in SEO and content (organic traffic $5-20 CAC long-term). Develop referral program (existing customers recruit new customers, typically $15-30 CAC). Paid ads provide speed and control—organic and referral provide efficiency and sustainability. Balance both.

If repeat rate too low (under 20%)

Product quality investigation: low repeat rate often signals disappointed first-purchase experience. Survey recent customers, read reviews honestly, test competitor products. If quality genuinely weaker, improve products before investing in retention marketing. Retention marketing amplifies experience—if experience disappointing, amplification counterproductive.

Basic retention program: email customers 30-45 days after first purchase (product replenishment reminder for consumables, complementary product suggestions for durables). Offer modest second-purchase discount (10-15% sufficient, not 25%+ training discount dependency). Include customer story or testimonial (builds community feeling). Implement loyalty program simple version—tenth purchase free or spending tier discounts.

If AOV stagnating or declining

Immediate tactics: add product bundles (buy coffee + filter together, save 15%). Implement free shipping threshold slightly above current AOV (if AOV $70, set threshold $85—encourages customers adding one more item). Show “customers also bought” recommendations on product pages (increase basket size). Create gift sets or curated collections (higher price points with perceived value).

Pricing review: small stores often under-price from fear of losing customers. Test 10-15% price increase on best-sellers—typically loses under 5% of customers while increasing revenue substantially. Add premium product tier (some customers willing to pay more for higher quality, but option doesn’t exist). Remove deep discounts (20%+ off trains customers to wait for sales, eroding regular-price AOV).

Simplified decision framework

Monthly metric review (15 minutes)

First week of month: calculate three metrics (CAC, repeat rate, AOV). Compare to previous month and three months ago. Identify biggest change—improved, declined, or flat. If improved: what caused it? Replicate success. If declined: what changed? Investigate and correct. If flat: which metric has most improvement opportunity? Prioritize that metric next month.

Document in spreadsheet or simple notes. Month-over-month comparison reveals patterns. Sudden CAC spike investigates timing (December CAC always higher—seasonal competition). Gradual AOV decline investigates product mix (customers buying cheaper items—need better upselling). Patterns inform decisions—one-time anomalies ignored, persistent trends addressed.

Quarterly deep review (1 hour)

Every 3 months: compare quarterly averages. Q1 versus Q4, Q2 versus Q1. Calculate trends: CAC rising, falling, or stable? Repeat rate improving or deteriorating? AOV growing or shrinking? Quarterly averages smooth monthly noise revealing true directional trends. Rising CAC trend requires action even if month-to-month acceptable. Declining repeat rate trend signals retention problem despite individual months appearing fine.

Set next-quarter focus based on trends. CAC rising 15% quarterly = acquisition efficiency focus (improve conversion, test channels, optimize creative). Repeat rate declining = retention focus (email campaigns, product quality, customer service). AOV flat = revenue optimization focus (bundles, pricing, upselling). One focus per quarter—small teams lack bandwidth for simultaneous multi-metric optimization.

Avoiding analysis paralysis

Track three metrics, ignore the rest initially

Analytics platforms show dozens of metrics. Small stores need three: CAC, repeat rate, AOV. Everything else is noise until these fundamentals optimized. Session duration, bounce rate, pageviews per session, time on site—interesting but not actionable for founder without technical team. Revenue per session, cart abandonment rate, newsletter open rate—secondary metrics addressed after fundamentals solid.

Advanced metrics require advanced actions. Cart abandonment rate reveals 70% abandon carts. So what? Reducing abandonment requires technical implementation—email automation, retargeting ads, checkout optimization. Small store founder lacks time and expertise implementing complex solutions. Focus metrics matching capability level—CAC reduction achieved through simple actions (pause bad ads, improve photos, test targeting). Repeat rate improved through basic email campaigns. AOV increased through product bundles.

Monthly updates sufficient, daily tracking unnecessary

Daily metric tracking creates false urgency. Sales down Tuesday versus Monday—meaningless. Orders down this week versus last week—could be random variation. Monthly patterns reveal truth—daily fluctuations just noise. Exception: daily operational monitoring (sales, orders, traffic) catches immediate problems (site down, ad campaign stopped, sudden traffic spike). Daily strategic metrics (CAC, LTV, retention) impossible—insufficient data points for meaningful calculation.

Monthly cadence prevents obsessive checking while maintaining awareness. First Monday monthly: calculate three metrics, document changes, identify focus area, implement one improvement. Remaining month: execute improvement, monitor operational metrics, avoid premature evaluation. Next month: evaluate whether improvement worked, adjust or continue. Rhythm prevents both neglect (never checking metrics) and obsession (checking metrics hourly achieving nothing).

Act on one insight per month maximum

Identifying ten problems tempts implementing ten solutions. Small team implementing ten changes simultaneously: half-implemented, poorly executed, impossible determining what worked. Better: implement one change fully, evaluate results, then implement next change. Monthly focus: CAC too high, implement better ad creative. Next month: evaluate results (CAC declined 20%, working), continue optimization. Following month: repeat rate low, implement retention email campaign. Next month: evaluate retention results.

Sequential optimization compounds. Month one: reduce CAC 15%. Month two: improve repeat rate 5 percentage points. Month three: increase AOV 12%. Month four: reduce CAC another 10%. Over four months: customer economics dramatically improved through focused sequential changes. Attempting simultaneous optimization: ten half-finished projects, minimal improvement, founder burnout.

When to upgrade analytics

Revenue milestone: $10k-15k monthly

Under $10k monthly revenue: simple tracking sufficient. Manual spreadsheet or basic platform reports adequate. Time investment justified improving product and marketing rather than analytics sophistication. Over $15k monthly revenue: business complexity justifies analytics investment. More products, more customers, more marketing channels—simple tracking insufficient capturing nuance. Investment in analytics tools ($50-100 monthly) pays for itself in better decision-making.

Team milestone: adding second person

Solo founder: personal tracking works (spreadsheet, dashboard checking, mental awareness). Adding team member (operations, marketing, VA): shared analytics required for alignment. Email-delivered reports ensure everyone sees same numbers without forwarding spreadsheets or granting dashboard access. Team analytics investment justified—coordination value exceeds tool cost.

Complexity milestone: multiple traffic sources

Single traffic source (organic only, Facebook ads only): simple platform reports show channel performance. Three-plus traffic sources (Facebook, Google, email, organic, influencer): channel-specific metrics required. Which channel drives highest LTV customers? Which channel lowest CAC? Simple reports insufficient answering these questions—upgraded analytics with source-tracking essential for budget allocation decisions.

Simple analytics checklist

Week one: setup (1 hour total)

Create simple spreadsheet: columns for Month, New customers, Total customers, Marketing spend, Revenue, Orders. Add formulas: CAC = marketing spend ÷ new customers, AOV = revenue ÷ orders. Determine data sources: Shopify reports, WooCommerce analytics, payment processor statements, ad platform reporting. Test pulling one month’s data ensuring can access everything needed. Document where each number comes from for future reference.

Monthly: data update (15 minutes)

First Monday of month: pull previous month data from platforms. Update spreadsheet with new month row. Calculate three metrics automatically via formulas. Compare to previous month—improved, declined, or flat? Note biggest change in simple comment. Set one improvement focus for current month based on data. That’s it—15 minutes monthly provides sufficient awareness for small store decision-making.

Quarterly: strategic review (1 hour)

Every three months: review trends across quarters. Calculate quarterly averages smoothing monthly variation. Identify directional trends—CAC rising or falling? Repeat rate improving or declining? AOV growing or shrinking? Set quarterly focus based on trends. Document learnings—what worked, what didn’t, what to try next quarter. One-hour quarterly investment maintains strategic direction while avoiding weekly analysis paralysis.

Frequently asked questions

What if I don’t have 90 days of data for repeat rate?

New stores (under 3 months old): skip repeat purchase rate temporarily. Focus entirely on customer acquisition cost and conversion rate—need customers before measuring retention. Month four onwards: begin calculating repeat rate for customers from month one. Month six: reliable repeat rate data available. Early-stage stores prioritize acquisition metrics, mature stores balance acquisition and retention metrics.

Should I track metrics by product or just overall?

Start overall—single number for CAC, single number for AOV, single number for repeat rate. Product-specific metrics require more data and time. Stores with 3-5 products: overall metrics sufficient. Stores with 10+ products in different categories: consider category-level tracking (coffee products versus equipment products). Product-level tracking (individual SKU metrics) only for stores with clear best-sellers generating 40%+ of revenue—focus on winners, ignore long tail initially.

Is $49/month for analytics too expensive for small stores?

Calculate time value. Dashboard checking takes 20-30 minutes daily = 10-15 hours monthly. At $30/hour (conservative founder time value), dashboard time costs $300-450 monthly. Email analytics tools ($49/month) save 10+ hours monthly = $300+ in time value. ROI positive if time worth $5+/hour. Better question: can afford NOT having automated analytics? Manual tracking consumes time better spent improving products and acquiring customers.